Evidence map›Paper›PMID 32521780›Full record

ReviewDiagnostics (Basel, Switzerland)2020

Shedding Light on Bladder Cancer Diagnosis in Urine.

Kit Man Chan, Jonathan Gleadle, Jordan Li, Krasimir Vasilev, Melanie MacGregor

Open access · goldAbstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.1field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 19 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Kit Man ChanSchool of Engineering, UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia.ORCID 0000-0001-8472-8514
Jonathan GleadleDepartment of Renal Medicine, Flinders Medical Centre, Bedford Park, SA 5042, Australia.
Jordan LiDepartment of Renal Medicine, Flinders Medical Centre, Bedford Park, SA 5042, Australia.
Krasimir VasilevSchool of Engineering, UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia.
Melanie MacGregorSchool of Engineering, UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia.ORCID 0000-0002-8671-181X
University of South Australia · AUFlinders University · AU

Funding

commonwealth CRC programUniversity of South Australia research theme investment scheme
6 · The paper itself

Abstract

Blue light cystoscopy (BLC) is the most recent clinical approach in the detection and diagnosis of bladder cancer, a common type of cancer with a high rate of recurrence. Representing a significant advance over previous approaches, this photodynamic diagnostic technique uses a photosensitiser prodrug as an adjunct to white light cystoscopy to enhance the in vivo detection of malignant tissues in the bladder based on their distinctive fluorescence. Whilst it does improve detection rates, BLC remains an invasive and costly procedure. Meanwhile, a variety of noninvasive urine detection methods and related microdevices have been developed, none of which have yet entered routine clinical use due to unsatisfactory sensitivity. Following a brief description of the current approaches and their limitations, we provide here a systematic review of a newer niche research aiming to develop a noninvasive adaptation of photodynamic diagnosis. The research to date surrounding the ex situ use of photosensitiser prodrugs for urinary diagnosis of bladder cancer is also discussed.

Indexed as

5-ALAbladder cancernoninvasivephotodynamic diagnosisurinary biomarkersurine

Identifiers

PMID32521780
PMCPMC7345106
OpenAlexW3033228754

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.